Despite all the developments to overcome MRI motion artifacts, there are still open questions. When do we need to repeat a scan? Is the image quality sufficient for segmentation or to make a diagnosis? Is the motion correction working properly? Independent of the type of image acquired (structural, diffusion, functional, etc.), machine learning algorithms can detect automatically motion artifacts and provide feedback in real time. In this work different machine learning algorithms have been tested to detect motion artifacts in synthetic and in vivo data.